1960 results about "Industrial robot" patented technology

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

Disclosed in the present invention is an obstacle avoidance method for an industrial robot in a complex environment, comprising the following steps: S1, performing model simplification on an industrial robot in a working space, and simplifying an obstacle into an obstacle point cloud; S2, placing a depth camera outside a motion space of the industrial robot in an "eye-to-hand" arrangement mode; and S3, before the industrial robot moves, using a G-APF-RRT* algorithm to plan a feasible path in the working space. In the present invention, the calculation mode of the distance between the industrial robot and the obstacle is simple, the required time is short, virtual joint interpolation is used and a spherical bounding box is used at a joint to completely envelope the industrial robot so as to simplify a model, and distance calculation can be completed only by calculating the distance between the center of sphere of the limited spherical bounding box and each point in the obstacle point cloud.

The invention provides an industrial robot autonomous collaborative decision-making method and system based on multi-modal sensing and a medium, and belongs to the technical field of industrial robot intelligent control. The method comprises the steps of performing cross-modal space-time alignment to eliminate data space-time differences by collecting visual, tactile and auditory information, realizing multi-modal feature fusion in combination with a dynamic weight adjustment mechanism and an attention calculation model, and generating a joint decision strategy through a deep learning optimization model. The system dynamically allocates sensor weights according to task types, introduces a multi-objective optimization mechanism of energy consumption, precision and safety, and sets a fault-tolerant rule to automatically recover the weights or recalibrate the sensors. According to the method, the problems of rigid data fusion and single optimization dimension in traditional multi-modal decision making are solved, and the precision, the response speed and the environmental adaptability of collaborative operation of the industrial robot are remarkably improved.

The invention relates to an industrial robot cooperative control method, system and device and a storage medium. The method comprises the following steps: acquiring machine system information, working environment conditions, task demand contents and cooperative mode information of a plurality of industrial robots; performing spatial analysis on the working environment condition to obtain environment structure characteristics; performing task decomposition on the task demand content according to the cooperation mode information to obtain a sub-task list; mapping the machine system information into the environment structure features to obtain a corresponding mapping relationship, and performing association analysis on the mapping relationship to obtain a task allocation scheme; performing individual optimization on the comprehensive control parameters based on a task allocation scheme to obtain robot control parameters; and performing cooperative strategy analysis according to the sub-task list and the robot control parameters to obtain robot cooperative strategies, and performing comprehensive analysis on all the robot cooperative strategies to obtain an overall cooperative control strategy. According to the invention, the whole industrial robot system can be more suitable for complex and changeable actual production scenes.

The invention provides an industrial robot distributed collaborative debugging method and system, and relates to the technical field of distributed control, and the method comprises the steps: building a directed acyclic graph model of an industrial robot collaborative debugging task, analyzing the directed acyclic graph model through a critical path method, and determining a critical path of the collaborative debugging task; reliability modeling is carried out on each industrial robot node in the collaborative debugging task, and the failure probability of each collaborative debugging sub-task under the current scheduling scheme is calculated in combination with the time redundancy characteristics of each collaborative debugging sub-task; according to the obtained directed acyclic graph model of the industrial robot collaborative debugging task, the key path of the collaborative debugging task and the failure probability of each collaborative debugging sub-task under the current scheduling scheme, defining a state space of a task scheduling scheme, searching the state space through a dynamic planning algorithm, and scheduling the task scheduling scheme according to the searched state space; and obtaining a scheduling scheme with the minimum global failure probability as an optimal fault-tolerant scheduling strategy at the current moment.

The invention discloses a mechanical arm grabbing control method and system based on digital twinning, and relates to the technical field of industrial robot intelligent control, and the mechanical arm grabbing control method comprises the steps that target object data and grabbing scene data are collected, and a digital twinning model of a target object and a grabbing scene is constructed; dynamic changes of the target object and the grabbing scene are monitored in real time, the digital twin model is dynamically corrected, and a high-precision digital twin model is generated; based on a high-precision digital twinborn model, extracting characteristics of a target object, and identifying an optimal grabbing point; generating a grabbing strategy based on the optimal grabbing point and the high-precision digital twin model, and verifying the feasibility of the grabbing strategy; and according to a verification result, dynamically executing an intelligent grabbing strategy. According to the method, the grabbing precision and stability of the mechanical arm are improved by correcting the digital twin model in real time and recognizing the optimal grabbing point.

The invention provides an industrial robot control system and method. The system comprises a trajectory planning module, a dynamic compensation module, a feedback control module, an execution module, a sensor module and a data storage module. The trajectory planning module generates an expected trajectory of an end effector of the robot; the dynamic compensation module calculates a dynamic compensation torque based on the expected trajectory and a robot dynamics model; the feedback control module generates feedback control torque according to the deviation between the real-time pose data acquired by the sensor module and the expected trajectory; the execution module drives robot joints to move according to the synthesized moment of the dynamic compensation moment and the feedback control moment; the sensor module collects robot joint angle, angular velocity and torque data in real time; the data storage module stores robot dynamic model parameters, control algorithm parameters and historical motion data. High-precision and smooth robot motion control can be achieved, the dynamic response and stability of the system are improved, and energy consumption and motion errors are reduced.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention discloses a track generation method and device for casting blank deburring and a medium, and relates to the technical field of grinding process.The track generation method mainly comprises the steps that according to FPFH feature descriptors of respective outer contours of an actual point cloud model and a standard point cloud model, key points matched with the standard point cloud model are searched through a KD tree, and the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model are obtained; performing multi-contour segmentation division of the actual point cloud model according to the distance between the key points; receiving polishing parameters input by the user based on the segmentation division results of the contours, and generating an initial track according to the polishing parameters; smoothing discrete trajectory points of the initial trajectory through B-spline interpolation, and generating a local obstacle avoidance path in combination with an industrial robot kinematics model and a fast expansion random tree star algorithm; outputting the final polishing track to a control terminal, and adjusting the track depth in real time according to the feedback of the force sensor. According to the method, regional differentiation track generation is supported, and the process adaptability and the personalized demand response capability of a complex contour are improved.

The invention discloses a welding robot track self-adaptive correction method based on multi-modal data fusion, and particularly relates to the technical field of industrial robots, which comprises the following steps: synchronously acquiring visual three-dimensional point cloud and arc current and voltage transient waveform data in a welding process in real time; an enhanced working trajectory feature vector fusing the visual trajectory features and the arc deviation compensation amount is constructed; respectively calculating real-time confidence coefficients of the visual sense and the arc sensing mode; inputting the confidence coefficient into a weight distribution strategy model, dynamically resolving vision and arc weight coefficients, and when the confidence coefficient of one sensor is low, lifting the weight of the other sensor to dominate correction; performing weighted fusion on the two pose adjustment amounts by using a weight coefficient to generate a six-degree-of-freedom comprehensive trajectory correction amount; and finally, the robot is driven to execute online adaptive trajectory correction. Through a confidence-driven dynamic fusion strategy, the limitation of a single sensor is overcome, and the welding robot trajectory tracking precision and the adaptive capacity are improved.

The invention provides an industrial robot multi-station intelligent sorting and stacking method based on visual guidance, which relates to the technical field of robot control, and comprises the following steps: determining a target station by acquiring multi-station real-time state information, calculating an optimal grabbing time window by using a graph neural network state predictor, and obtaining a target grabbing time window; and the placement strategy is dynamically adjusted based on the visual image and the force sensing data in the article stacking process. According to the multi-station intelligent stacking system, the sorting efficiency in a multi-station scene can be improved, the grabbing precision of dynamic objects is enhanced, and stable and reliable intelligent stacking operation is achieved.

A method of programming an industrial robot (100), composed of a robot manipulator (110) and a robot controller (120), comprises: recording movements of the robot manipulator during a kinesthetic programming session, for thereby obtaining a robot trajectory; capturing speech data while recording the movements of the robot manipulator; decomposing the recorded speech data into a plurality of phases; for each phase of the speech data, parsing the speech data into robot parameter values using a natural-language model (170), identifying a contemporaneous phase of the robot trajectory and annotating it with the robot parameter values; and, on the basis of the annotated robot trajectory, generating a robot program executable by the robot controller.

The invention provides an industrial robot automatic control method and device and a computer readable storage medium, and the method comprises the steps: extracting the surface features of a to-be-polished workpiece through an image recognition technology, and controlling the initial force and torque of an end effector of an industrial robot based on the surface features of the to-be-polished workpiece; the surface temperature fluctuation of the workpiece in the to-be-polished process is monitored through a temperature sensor of the end effector of the industrial robot, and then the force and torque of the end effector of the industrial robot are adjusted; and power and state data in the polishing operation process are collected in real time, whether abnormal conditions exist or not is pre-judged through the deep learning technology, and the polishing operation is stopped immediately once the abnormal conditions are found. By means of the method and the corresponding device, the industrial robot can achieve intelligent automatic adjustment in the grinding and polishing process, and high performance and high reliability are kept under the complex working condition.

The embodiment of the invention provides an industrial robot autonomous assembly task planning method based on a multi-agent large model, and belongs to the technical field of automatic assembly. The method comprises the steps of obtaining assembly task data of an assembly object, wherein the assembly task data comprises a task instruction for assembling the assembly object and an assembly manual; analyzing the task instruction and the assembly manual to obtain task execution logic; performing real-time acquisition on an assembly environment when the assembly object is assembled to obtain a target image; multi-modal sensing is conducted on the target image, a sensing result is obtained, and the sensing result comprises the position of at least one assembly of the assembly object in the assembly environment; and converting the task execution logic and the perception result into a planning domain definition language format, and performing assembly task planning through the behavior tree to obtain a target assembly task. According to the embodiment of the invention, the assembly efficiency and precision of the industrial robot are improved.

The invention discloses an industrial robot intelligent control system which comprises a master control terminal, the master control terminal is provided with an industrial-grade multi-core processor and is integrated with a motion control subsystem and an environment sensing subsystem, the motion control subsystem is connected with the master control terminal through a first communication interface, and the environment sensing subsystem is connected with the master control terminal through a second communication interface. According to the motion control subsystem, a distributed axis controller array and reinforcement learning strategy switching algorithm is adopted, so that the trajectory tracking precision and the energy consumption efficiency are improved; and the environment sensing subsystem realizes real-time anomaly detection through multi-mode sensor fusion and an LSTM-GAN fault prediction model. The system supports containerized deployment and edge computing collaboration and integrates AR human-computer interaction, and the adaptive capacity and safety of the industrial robot are remarkably improved.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention relates to the technical field of industrial automation, in particular to an industrial robot remote monitoring method and system based on the Internet of Things, and the method comprises the following steps: extracting a load state and transmission link delay through the operation data of an industrial robot node, comparing the communication performance with task load distribution, and weighting link relevance; and generating a node association topological structure. According to the method, the operation data of the industrial robot is deeply analyzed, the production efficiency is improved, the system response speed is increased, the load states and link delays of the nodes are extracted, communication between the nodes and task load distribution are optimized, the task execution sequence is optimized through a data-driven strategy, real-time state changes are coped through dynamic adjustment, and therefore the real-time state change of the industrial robot is optimized. The delay and failure rate are remarkably reduced, the link and node capacity is continuously monitored, the task allocation reasonability is ensured, the problem can be quickly positioned, shutdown is reduced, and the stability and predictability of the production process are improved by optimizing the task path and adjusting cloud edge cooperation.

The invention relates to the technical field of robot welding, in particular to an industrial robot auxiliary welding device which comprises a mounting base. The welding unit is connected with the mounting base and is used for realizing multi-angle welding of the fixed workpiece; the circulating fixing unit is arranged on the outer side of the welding unit, is connected with the mounting base, and is used for independently supporting a plurality of workpieces and automatically fixing and unbinding the workpieces during station switching so as to realize circulating machining; wherein the circulating fixing unit comprises an induction type automatic fixing assembly, a self-locking type driving control assembly and an auxiliary cleaning assembly, by arranging the circulating fixing unit, a workpiece can be automatically fixed, constraint to the welded workpiece can be automatically relieved, feeding and discharging operation can be conveniently conducted, fixing does not need to be completed manually in the whole machining process, and the machining efficiency is improved. Manual operation procedures are reduced, and workpiece production efficiency is improved.

The invention relates to the field of industrial automation, in particular to an industrial robot glass processing system based on large model intelligent decision, and relates to an industrial robot key technology based on multi-source information integration and intelligent decision. Glass cutting is optimized through multi-physics field monitoring, the edge defects are reduced by 70%, and the rejection rate is reduced by 50%. And the edge grinding of the special-shaped glass is assisted by three-dimensional reconstruction, and the precision deviation is controlled within 0.1 mm. And multi-modal data improves the coating uniformity, the thickness deviation is within 5 [mu] m, and the film layer shedding is reduced by 80%. Multi-robot cooperation is achieved through reinforcement learning, the machining efficiency is improved by 60%, and the cost is reduced by 40%. And a digital twinning optimization process is adopted, so that the equipment failure rate is reduced by 60%.

The invention discloses a shaping robot, a shaping system and a grain processing method thereof. An integrated moving and transferring device is used as a chassis for bearing all modules; the shaping industrial robot is used for driving a cutter on the mechanical arm to conduct shaping machining. The medicine scrap sucking and cleaning device is used for cleaning scraps generated by processing; the tool changing magazine is used for placing and managing tools and comprises a quick-changing tool rest, and a detection sensor used for detecting whether the tools exist or not is arranged on the quick-changing tool rest; the control system comprises a coordinate system management module, a tool path planning module, a robot kinematics module, a collision detection module, a motion simulation module and a robot program generation module. Automatic machining positioning, automatic machining path planning and automatic cutting tool switching and management can be achieved; the automation degree of grain cutting machining is effectively improved; in the shaping process, temperature and contact force detection sensors are used for detection, and early warning protection in the machining process is achieved.

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.

The invention relates to the field of industrial automation, and particularly provides a semiconductor photoetching system based on an intelligent industrial robot. And a multi-sensor fusion and real-time compensation algorithm is adopted, so that photoetching positioning errors are accurately sensed and corrected, the positioning precision is greatly improved, and the stable performance of the chip is ensured. According to a multi-physical field monitoring and self-adaptive control algorithm, process parameters are dynamically regulated and controlled according to a photoetching environment and a photoresist state, pattern transfer is optimized, pattern defects are avoided, and photoetching pattern quality is improved. According to the multi-modal data fusion and deep learning algorithm, photoresist coating parameters are automatically adjusted according to the surface characteristics of the wafer, uniform coating is realized, and the stability of the photoetching process is enhanced. The reinforcement learning algorithm assists the multiple robots in efficient collaboration, optimizes task allocation, improves the overall photoetching efficiency and the resource utilization rate, and promotes the progress of the semiconductor photoetching technology in all directions.

The invention relates to the technical field of industrial robots, in particular to an industrial robot automatic testing method and system, and the method comprises the following steps: based on a robot communication interface, collecting a communication signal of a robot, measuring the delay of signal transmission, verifying the integrity of transmission data, and evaluating the quality of a communication link; and generating a signal quality analysis result. According to the method, accurate evaluation of the communication quality of the robot and construction of a path planning task and a dynamic test environment are realized by collecting communication signals, deep evaluation of the path planning capability of the robot is provided, and measurement of the execution precision of the robot is realized by combining deviation identification of an actual execution path and a target path; a sensor is used for collecting vibration data under various loads, abnormal vibration of the robot under load changes is monitored in real time by analyzing vibration amplitude and frequency changes, the stability and performance of the robot under various load conditions are diagnosed, and the load bearing capacity of the robot is judged.

The invention relates to the technical field of bearings, and particularly discloses a high-precision cylindrical roller bearing for an industrial robot, the high-precision cylindrical roller bearing comprises a first shaft sleeve, a second shaft sleeve and a spacer bush, the first shaft sleeve and the second shaft sleeve are located on the inner side and the outer side of the spacer bush respectively, a roller is arranged on the surface of the spacer bush, and the outer surface of the roller is in rotating contact with the first shaft sleeve and the second shaft sleeve respectively; and a compensation assembly is arranged on the inner side of the spacer bush and comprises a guide groove formed in the inner side of the spacer bush, a movable frame is slidably connected to the inner side of the guide groove, and a movable plate is fixedly connected to the outer surface of the movable frame. By arranging the compensation assembly, when the lubricating wheel is abraded, the movable frame moves towards one side close to the roller under the elastic force action of the double metal plates, and the reciprocating lead screw drives the lubricating wheel to synchronously move, so that the lubricating wheel is always attached to the surface of the roller, and a good lubricating effect can be continuously achieved on the bearing; and the influence of abrasion of the lubricating wheel on the lubricating effect of the bearing is reduced.

The invention provides an industrial robot safety monitoring method and system, and relates to the technical field of safety monitoring, and the method comprises the steps: arranging a multi-mode sensor array to collect the state information of a robot; performing empirical mode decomposition on the vibration signal to obtain an intrinsic mode function reflecting the abrasion degree of the robot transmission system; performing wavelet transform on the temperature signal to obtain a temperature feature set reflecting the heat dissipation condition of the robot; through knowledge extraction, knowledge representation and knowledge fusion technologies, semantic representation and linking are carried out on various knowledge in robot components, performance parameters, degradation modes and maintenance measures, and a management knowledge graph corresponding to the robot is constructed; and performing reasoning mining on the management knowledge graph based on a rule mining algorithm of path sorting, extracting degradation characteristics corresponding to each component of the robot in the management knowledge graph by mining a reasoning path formed by frequently co-occurring entities and relationships, and predicting a health state trend of the robot in combination with a pre-constructed time sequence prediction model.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

The invention discloses an industrial robot control method based on deep learning, and relates to the technical field of industrial robot control. Comprising the steps of collecting operation data of a robot in a working process in real time through a sensor; dynamically adjusting control parameters according to real-time data changes; based on the reinforcement learning model, the complexity of the current task and the robot capability are analyzed in real time, and cooperative tasks among the multiple robots are intelligently distributed; the running state of the robot is continuously monitored, and early warning is given out when abnormity is found; according to the invention, real-time analysis and intelligent allocation are carried out on the task complexity and the robot capability through the reinforcement learning model, the task cooperation efficiency is optimized, and the resource utilization rate is improved; according to the method, an abnormal detection and early warning mechanism is constructed, an operation deviation state is rapidly identified, when an abnormity occurs, the abnormity is fed back to a control system through a graded early warning signal, task redistribution or parameter adjustment is carried out, and the self-adaptability, the cooperation capability and the operation safety of the industrial robot in a complex scene are remarkably improved.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.